ArticleBMC medical research methodology2016
Network or regression-based methods for disease discrimination: a comparison study.
Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- On the usage of artificial intelligence in leprosy care: A systematic literature review.PLoS computational biology · 2025Pooled it
- The influence of single nucleotide polymorphisms of NOD2 or CD14 on the risk of Mycobacterium tuberculosis diseases: a systematic review.Systematic reviews · 2021Pooled it
- Pre-exposure Prophylaxis (PrEP) Awareness and Engagement Among MSM at High Risk of HIV Infection in China: A Multi-City Cross-Sectional Survey.AIDS and behavior · 2025Article
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer.Frontiers in oncology · 2024Article
- Risk factors assessment and a Bayesian network model for predicting ischemic stroke in patients with cardiac myxoma.Frontiers in cardiovascular medicine · 2023Article
- Prediction of the occurrence of leprosy reactions based on Bayesian networks.Frontiers in medicine · 2023Article
- Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease.Smart health (Amsterdam, Netherlands) · 2022Article
- Application of Network Analysis to Uncover Variables Contributing to Functional Recovery after Stroke.Brain sciences · 2022Article
- Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy.Frontiers in neuroscience · 2022Article
- Machine-learning models predicting osteoarthritis associated with the lead blood level.Environmental science and pollution research international · 2021Article
- Contributions and limitations of using machine learning to predict noise-induced hearing loss.International archives of occupational and environmental health · 2021Review
- Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants.Frontiers in pediatrics · 2021Article
- Potential of a machine-learning model for dose optimization in CT quality assurance.European radiology · 2019Article
- Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification.Scientific reports · 2019Article
- Identification of swallowing disorders in early and mid-stage Parkinson's disease using pattern recognition of pharyngeal high-resolution manometry data.Neurogastroenterology and motility · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform.
methodsSimulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines.
resultsThe simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods.
conclusionAlthough regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.